Diagnostic Machine Learning Algorithm to Identify MEG Features of Mild TBI and Comorbid PTSD

Va San Diego Healthcare System, San Diego CA

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Abstract

Mild traumatic brain injury (mTBI) is a leading cause of sustained physical, cognitive, emotional, and behavioral deficits in OEF/OIF/OND Veterans and the general public. However, the underlying pathophysiology and recovery mechanisms, especially those associated with cognitive functioning in mTBI, are not completely understood. The neuronal mechanisms for the increased risk of PTSD after an mTBI are even less clear. Conventional MRI and CT images are generally negative even in patients with persistent post-concussive symptoms (PCS) and/or PTSD symptoms. Diffusion-based MRI techniques have been developed to identify abnormalities in white-matter tracts, owing to the major role of diffuse axonal injury (DAI) in mTBI. Yet even sophisticated diffusion-based MRI techniques are not sufficiently sensitive for reliable clinical applications. Recent animal studies indicate that gray matter is also vulnerable to DAI, which leads to abnormal electromagnetic signals from the injured regions. In this regard, support is mounting for the sensitivity of resting-state magnetoencephalography (rs-MEG) source imaging markers for detecting neuronal abnormalities in mTBI. We demonstrated that rs-MEG delta-wave (1-4 Hz) markers were very sensitive in distinguishing mTBI patients with persistent PCS from neurologically intact individuals. We also found that rs-MEG gamma- band (30-80 Hz) markers show marked hyperactivity in mTBI, possibly due to injury of GABA-ergic parvalbumin-positive (PV+) interneurons. In addition, we found that task-evoked MEG (te-MEG) recordings during working memory (WM) task detected abnormal signals throughout the brain in mTBI that were related to poorer cognitive functioning. A main goal of this application is to develop highly sensitive diagnostic algorithms to differentiate Veterans with mTBI from those with comorbid mTBI and PTSD, and those healthy control Veterans. The new approaches will use artificial neural network based machine-learning techniques to integrate rs-MEG and te-MEG imaging makers. We will study three groups of Veterans (N=75 per group): 1) individuals with mTBI and persistent PCS (mTBI-only group); 2) individuals with comorbid mTBI and PTSD who have persistent PCS and PTSD symptoms; 3) healthy controls (HC). Aim 1 will establish a machine- learning based MEG diagnostic algorithm for mTBI that optimally integrates three MEG regional imaging markers (i.e., delta-band and gamma-band rs-MEG; WM evoked MEG) to differentiate Veterans with mTBI (mTBI-only and comorbid mTBI-PTSD) from HC Veterans with >90% accuracy. We predict that sensitive features for mTBI classification will include abnormal increases in rs-MEG delta- and gamma-band activity in prefrontal and posterior-parietal areas and aberrant WM evoked activity in the mTBI-only and comorbid groups relative to the HC group. Aim 2 will develop a machine-learning MEG algorithm that integrates rs-MEG activity and te-MEG responses evoked by a negative emotion processing picture (NEPP) task to differentiate Veterans with mTBI-only from those with comorbid mTBI-PTSD with > 90% accuracy. We predict that comorbid mTBI- PTSD group will show increases in rs-MEG (beta-band) and NEPP te-MEG activity from amygdala and decreases in activity from ventromedial prefrontal cortex (vmPFC), dorsolateral PFC (dlPFC), and precuneus over the mTBI-only group. Aim 3 will examine the correlates of abnormal MEG-based neurophysiological features in mTBI-only and comorbid mTBI-PTSD with clinical symptoms, cognitive impairments, and real-world quality of life. We predict that rs-MEG and WM te-MEG in specific prefrontal and posterior parietal areas will correlate with PCS symptoms and cognitive deficits. In the comorbid mTBI-PTSD group, PTSD symptoms will correlate with abnormal rs-MEG and NEPP te-MEG hyperactivity in the amygdala, and abnormal rs-MEG hypoactivity in the vmPFC, dlPFC, and precuneus. The success of this project will significantly improve neuroimaging-based techniques that can effectively aid in the diagnosis of mTBI and better characterize the relationships among neurobiological, neuropsychological, and neuropsychiatric effects of mTBI and PTSD.